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Full-Text Articles in Engineering
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Minimum Distance Estimation For Time Series Analysis With Little Data, Hakan Tekin
Theses and Dissertations
Minimum distance estimate is a statistical parameter estimate technique that selects model parameters that minimize a good-of-fit statistic. Minimum distance estimation has been demonstrated better standard approaches, including maximum likelihood estimators and least squares, in estimating statistical distribution parameters with very small data sets. This research applies minimum distance estimation to the task of making time series predictions with very few historical observations. In a Monte Carlo analysis, we test a variety of distance measures and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when …
A New Goodness-Of-Fit Test For The Gamma Distribution Based On Sample Spacings From Complete And Censored Samples, Huseyin Duman
A New Goodness-Of-Fit Test For The Gamma Distribution Based On Sample Spacings From Complete And Censored Samples, Huseyin Duman
Theses and Dissertations
This thesis studies a new goodness-of-fit test for the gamma distribution with known shape parameter. This test statistic, Z*, is based on spacings from complete or censored samples. The size of samples varied between 5 and 35. The critical value tables were generated for the Z* test statistic for complete and censored samples. The critical values were obtained for five different significance levels: 0.20 0.15, 0.10, 0.05, and 0.01. An extensive power study, containing 50,000 Monte Carlo runs was conducted using nine alternative distributions, Ha. It was observed that the Z* test statistic was more powerful against certain …
Modified Goodness-Of-Fit Tests For The Inverse Gaussian Distribution With Two Unknown Parameter, Huseyin Gunes
Modified Goodness-Of-Fit Tests For The Inverse Gaussian Distribution With Two Unknown Parameter, Huseyin Gunes
Theses and Dissertations
Modified Kolmogorov- Smirnov (KS), Anderson-Darling (AD), Cramer-von Mises (CV), Kupier (V), and Watson (W) goodness-of-fit tests are generated for the inverse Gaussian distribution with unknown parameters. The inverse Gaussian parameters are estimated by maximum likelihood estimation. A Monte Carlo simulation of 50,000 repetitions is used to generate critical values for sample sizes of 5 through 50 with an increment of five, sample sizes of 60 through 100 with an increment of 10, and 24 different values of the inverse Gaussian shape parameter. A 50,000-repetition Monte Carlo power study is carried out using data with sample sizes of 5 through 100 …
Modified Anderson-Darling And Cramer-Von Mises Goodness-Of-Fit Tests For The Normal Distribution, David A. Gwinn Sr.
Modified Anderson-Darling And Cramer-Von Mises Goodness-Of-Fit Tests For The Normal Distribution, David A. Gwinn Sr.
Theses and Dissertations
New techniques for calculating goodness-of-fit statistics for normal distributions with parameters estimated from the sample are investigated. Samples are generated for a Normal(0,1) distribution. Critical values are calculated for five modifications to the Anderson-Darling statistic and five modifications to the Cramer-Von Mises statistic. An extensive power study is done to test the power of the new statistics versus the power of the unmodified statistics. Powers of six of the new statistics show minimal to no improvement, two of the new statistics show a marked decrease in power, and two of the new statistics show an overall increase in power over …
A Modified Chi-Squared Goodness-Of-Fit Test For The Three-Parameter Gamma Distribution With Unknown Parameters, Thomas J. Sterle
A Modified Chi-Squared Goodness-Of-Fit Test For The Three-Parameter Gamma Distribution With Unknown Parameters, Thomas J. Sterle
Theses and Dissertations
A modified chi-squared goodness-of-fit test was created for the gamma distribution in the case where all three parameters must be estimated from the sample. Critical values are generated using a Monte Carlo simulation procedure with 5000 repetitions each. Random samples of 8 different sizes were drawn from gamma distributions with shape parameters 1, 1.5, 2., and 2.5. The shape, scale, and location parameters were then estimated from each sample, using an iterative technique combining the maximum likelihood and minimum distance methods, enabling, computation of the chi-squared statistics and critical values. The same process is used to generate random samples, parameter …
A Modified Anderson Darling Goodness-Of-Fit Test For The Gamma Distribution With Unknown Scale And Location Parameters, Tamer Ozmen
Theses and Dissertations
A new modified Anderson-Darling goodness-of-fit test is introduced for the three-parameter Gamma distribution when the location parameter is found by minimum distance estimation and scale parameter by maximum likelihood estimation. Monte Carlo simulation studies were performed to calculate the critical values for A-D test when A-D statistic is minimized. These critical values are then used for testing whether a set of observations follows a Gamma distribution when the scale and location parameters axe unspecified and are estimated from the sample. Functional relationship between the critical values of A-D is also examined for each shape parameter by the variables, sample size …